Automated analysis of neurite branching in cultured cortical neurons using HCA-Vision

From pixels to connections: exploring in vitro neuron reconstruction software for network graph generation

Digital reconstruction has been instrumental in deciphering how in vitro neuron architecture shapes information flow. Emerging approaches reconstruct neural systems as networks with the aim of understanding their organization through graph theory. Computational tools dedicated to this objective build models of nodes and edges based on key cellular features such as somata, axons, and dendrites. Fully automatic implementations of these tools are readily available, but they may also be purpose-built from specialized algorithms in the form of multi-step pipelines. Here we review software tools informing the construction of network models, spanning from noise reduction and segmentation to full network reconstruction. The scope and core specifications of each tool are explicitly defined to assist bench scientists in selecting the most suitable option for their microscopy dataset. Existing tools provide a foundation for complete network reconstruction, however more progress is needed in establishing morphological bases for directed/weighted connectivity and in software validation.

Significant Sex Differences in the Efficacy of the CSF1R Inhibitor-PLX5622 on Rat Brain Microglia Elimination

Microglia play pivotal roles in central nervous system development, homeostasis, responses to trauma, and neurodegenerative and neuropsychiatric disorders with significant sex-bias in their symptoms and prevalence. Survival of the microglia in adult brains depends on the expression of the colony-stimulating factor 1 receptor (CSF1R). The inhibition of CSF1R by brain-permeant PLX5622 in the chow eliminates, within 5–10 days, ~90% of the microglia in female and male mice, thereby enabling the investigation of the roles of the microglia in health and pathological mice models. Because of a prevailing “impression” that PLX5622 is ineffective in rats, it has hardly been used in studies of adult rats. Here, we report that effective microglia elimination by PLX5622-chow in rats is highly sex-dependent. Our observations provide missing information for the limited use and interpretation of PLX5622 in biomedical studies of the microglia in rat models. The sex differences that are too often overlooked must be carefully considered and clearly emphasized.

NeuriteNet: A convolutional neural network for assessing morphological parameters of neurite growth

BACKGROUND

During development or regeneration, neurons extend processes (i.e., neurites) via mechanisms that can be readily analyzed in culture. However, defining the impact of a drug or genetic manipulation on such mechanisms can be challenging due to the complex arborization and heterogeneous patterns of neurite growth in vitro. New Method: NeuriteNet is a Convolutional Neural Network (CNN) sorting model that uses a novel adaptation of the XRAI saliency map overlay, which is a region-based attribution method. NeuriteNet compares neuronal populations based on differences in neurite growth patterns, sorts them into respective groups, and overlays a saliency map indicating which areas differentiated the image for the sorting procedure.

RESULTS

In this study, we demonstrate that NeuriteNet effectively sorts images corresponding to dissociated neurons into control and treatment groups according to known morphological differences. Furthermore, the saliency map overlay highlights the distinguishing features of the neuron when sorting the images into treatment groups. NeuriteNet also identifies novel morphological differences in neurons cultured from control and genetically modified mouse strains. Comparison with Existing Methods: Unlike other neurite analysis platforms, NeuriteNet does not require manual manipulations, such as segmentation of neurites prior to analysis, and is more accurate than experienced researchers for categorizing neurons according to their pattern of neurite growth.

CONCLUSIONS

NeuriteNet can be used to effectively screen for morphological differences in a heterogeneous group of neurons and to provide feedback on the key features distinguishing those groups via the saliency map overlay.

Image-derived models of cell organization changes during differentiation and drug treatments

PC12 cells are a popular model system to study changes driving and accompanying neuronal differentiation. While attention has been paid to changes in transcriptional regulation and protein signaling, much less is known about the changes in organization that accompany PC12 differentiation. Fluorescence microscopy can provide extensive information about these changes, although it is difficult to continuously observe changes over many days of differentiation. We describe a generative model of differentiation-associated changes in cell and nuclear shape and their relationship to mitochondrial distribution constructed from images of different cells at discrete time points. We show that the model accurately represents complex cell and nuclear shapes and learn a regression model that relates cell and nuclear shape to mitochondrial distribution; the predictive accuracy of the model increases during differentiation. Most importantly, we propose a method, based on cell matching and interpolation, to produce realistic simulations of the dynamics of cell differentiation from only static images. We also found that the distribution of cell shapes is hollow: most shapes are very different from the average shape. Finally, we show how the method can be used to model nuclear shape changes of human-induced pluripotent stem cells resulting from drug treatments.

Long-term development of human iPSC-derived pyramidal neurons quantified after transplantation into the neonatal mouse cortex

One of the main obstacles for studying the molecular and cellular mechanisms underlying human neurodevelopment in vivo is the scarcity of experimental models. The discovery that neurons can be generated from human induced pluripotent stem cells (hiPSCs) paves the way for novel approaches that are stem cell-based. Here, we developed a technique to follow the development of transplanted hiPSC-derived neuronal precursors in the cortex of mice over time. Using post-mortem immunohistochemistry we quantified the differentiation and maturation of dendritic patterns of the human neurons over a total of six months. In addition, entirely hiPSC-derived neuronal parenchyma was followed over eight months using two-photon in vivo imaging through a cranial window. We found that transplanted hiPSC-derived neuronal precursors exhibit a "protracted" human developmental programme in different cortical areas. This offers novel possibilities for the sequential in vivo study of human cortical development and its alteration, followed in "real time".

Automated Neuron Detection in High-Content Fluorescence Microscopy Images Using Machine Learning

The study of neuronal morphology in relation to function, and the development of effective medicines to positively impact this relationship in patients suffering from neurodegenerative diseases, increasingly involves image-based high-content screening and analysis. The first critical step toward fully automated high-content image analyses in such studies is to detect all neuronal cells and distinguish them from possible non-neuronal cells or artifacts in the images. Here we investigate the performance of well-established machine learning techniques for this purpose. These include support vector machines, random forests, k-nearest neighbors, and generalized linear model classifiers, operating on an extensive set of image features extracted using the compound hierarchy of algorithms representing morphology, and the scale-invariant feature transform. We present experiments on a dataset of rat hippocampal neurons from our own studies to find the most suitable classifier(s) and subset(s) of features in the common practical setting where there is very limited annotated data for training. The results indicate that a random forests classifier using the right feature subset ranks best for the considered task, although its performance is not statistically significantly better than some support vector machine based classification models.

A Fragment of Apolipoprotein E4 Leads to the Downregulation of a CXorf56 Homologue, a Novel ER-Associated Protein, and Activation of BV2 Microglial Cells

Despite the fact that harboring the apolipoprotein E4 (APOE4) allele represents the single greatest risk factor for late-onset Alzheimer's disease (AD), the exact mechanism by which apoE4 contributes to disease progression remains unknown. Recently, we demonstrated that a 151 amino-terminal fragment of apoE4 (nApoE4_1-151) localizes within the nucleus of microglia in the human AD brain, suggesting a potential role in gene expression. In the present study, we investigated this possibility utilizing BV2 microglia cells treated exogenously with nApoE4_1-151. The results indicated that nApoE4_1-151 leads to morphological activation of microglia cells through, at least in part, the downregulation of a novel ER-associated protein, CXorf56. Moreover, treatment of BV2 cells with nApoE4_1-151 resulted in a 68-fold increase in the expression of the inflammatory cytokine, TNFα, a key trigger of microglia activation. In this regard, we also observed a specific binding interaction of nApoE4_1-151 with the TNFα promoter region. Collectively, these data identify a novel gene-regulatory pathway involving CXorf56 that may link apoE4 to microglia activation and inflammation associated with AD.

Validation and Optimization of an Image-Based Screening Method Applied to the Study of Neuronal Processes on Nanogrooves

Research on neuronal differentiation and neuronal network guidance induced through nanotopographical cues generates large datasets, and therefore the analysis of such data can be aided by automatable, unbiased image screening tools. To link such tools, we present an image-based screening method to evaluate the influence of nanogroove pattern dimensions on neuronal differentiation. This new method consists of combining neuronal feature detection software, here HCA-Vision, and a Frangi vesselness algorithm to calculate neurite alignment values and quantify morphological aspects of neurons, which are measured via neurite length, neuronal polarity, and neurite branching, for differentiated SH-SY5Y cells cultured on nanogrooved polydimethylsiloxane (PDMS) patterns in the 200–2000 nm range. The applicability of this method is confirmed by our results, which find that the level of alignment is dependent on nanogroove dimensions. Furthermore, the screening method reveals that differentiation and alignment are correlated. In particular, patterns with groove widths >200 nm and with a low ridge width to pattern period ratio have a quantifiable influence on alignment, neurite length, and polarity. In summary, the novel combination of software that forms a base for this statistical analysis method demonstrates good potential for evaluating tissue microarchitecture, which depends on subtle design variation in substrate topography. Using the screening method, we obtained automated and sensitive quantified readouts from large datasets.

Automated sorting of neuronal trees in fluorescent images of neuronal networks using NeuroTreeTracer

Fluorescence confocal microscopy has become increasingly more important in neuroscience due to its applications in image-based screening and profiling of neurons. Multispectral confocal imaging is useful to simultaneously probe for distribution of multiple analytes over networks of neurons. However, current automated image analysis algorithms are not designed to extract single-neuron arbors in images where neurons are not separated, hampering the ability map fluorescence signals at the single cell level. To overcome this limitation, we introduce NeuroTreeTracer - a novel image processing framework aimed at automatically extracting and sorting single-neuron traces in fluorescent images of multicellular neuronal networks. This method applies directional multiscale filters for automated segmentation of neurons and soma detection, and includes a novel tracing routine that sorts neuronal trees in the image by resolving network connectivity even when neurites appear to intersect. By extracting each neuronal tree, NeuroTreetracer enables to automatically quantify the spatial distribution of analytes of interest in the subcellular compartments of individual neurons. This software is released open-source and freely available with the goal to facilitate applications in neuron screening and profiling.

iDrugs and iDevices Discovery Research: Preclinical Assays, Techniques, and Animal Model Studies for Ocular Hypotensives and Neuroprotectants

Discovery ophthalmic research is centered around delineating the molecular and cellular basis of ocular diseases and finding and exploiting molecular and genetic pathways associated with them. From such studies it is possible to determine suitable intervention points to address the disease process and hopefully to discover therapeutics to treat them. An investigational new drug (IND) filing for a new small-molecule drug, peptide, antibody, genetic treatment, or a device with global health authorities requires a number of preclinical studies to provide necessary safety and efficacy data. Specific regulatory elements needed for such IND-enabling studies are beyond the scope of this article. However, to enhance the overall data packages for such entities and permit high-quality foundation-building publications for medical affairs, additional research and development studies are always desirable. This review aims to provide examples of some target localization/verification, ocular drug discovery processes, and mechanistic and portfolio-enhancing exploratory investigations for candidate drugs and devices for the treatment of ocular hypertension and glaucomatous optic neuropathy (neurodegeneration of retinal ganglion cells and their axons). Examples of compound screening assays, use of various technologies and techniques, deployment of animal models, and data obtained from such studies are also presented.

Improved detection of soma location and morphology in fluorescence microscopy images of neurons

BACKGROUND

Automated detection and segmentation of somas in fluorescent images of neurons is a major goal in quantitative studies of neuronal networks, including applications of high-content-screenings where it is required to quantify multiple morphological properties of neurons. Despite recent advances in image processing targeted to neurobiological applications, existing algorithms of soma detection are often unreliable, especially when processing fluorescence image stacks of neuronal cultures.

NEW METHOD

In this paper, we introduce an innovative algorithm for the detection and extraction of somas in fluorescent images of networks of cultured neurons where somas and other structures exist in the same fluorescent channel. Our method relies on a new geometrical descriptor called Directional Ratio and a collection of multiscale orientable filters to quantify the level of local isotropy in an image. To optimize the application of this approach, we introduce a new construction of multiscale anisotropic filters that is implemented by separable convolution.

RESULTS

Extensive numerical experiments using 2D and 3D confocal images show that our automated algorithm reliably detects somas, accurately segments them, and separates contiguous ones.

COMPARISON WITH EXISTING METHODS

We include a detailed comparison with state-of-the-art existing methods to demonstrate that our algorithm is extremely competitive in terms of accuracy, reliability and computational efficiency.

CONCLUSIONS

Our algorithm will facilitate the development of automated platforms for high content neuron image processing. A Matlab code is released open-source and freely available to the scientific community.

A robust method for high-precision quantification of the complex three-dimensional vasculatures acquired by X-ray microtomography

The quantification of micro-vasculatures is important for the analysis of angiogenesis on which the detection of tumor growth or hepatic fibrosis depends. Synchrotron-based X-ray computed micro-tomography (SR-µCT) allows rapid acquisition of micro-vasculature images at micrometer-scale spatial resolution. Through skeletonization, the statistical features of the micro-vasculature can be extracted from the skeleton of the micro-vasculatures. Thinning is a widely used algorithm to produce the vascular skeleton in medical research. Existing three-dimensional thinning methods normally emphasize the preservation of topological structure rather than geometrical features in generating the skeleton of a volumetric object. This results in three problems and limits the accuracy of the quantitative results related to the geometrical structure of the vasculature. The problems include the excessively shortened length of elongated objects, eliminated branches of blood vessel tree structure, and numerous noisy spurious branches. The inaccuracy of the skeleton directly introduces errors in the quantitative analysis, especially on the parameters concerning the vascular length and the counts of vessel segments and branching points. In this paper, a robust method using a consolidated end-point constraint for thinning, which generates geometry-preserving skeletons in addition to maintaining the topology of the vasculature, is presented. The improved skeleton can be used to produce more accurate quantitative results. Experimental results from high-resolution SR-µCT images show that the end-point constraint produced by the proposed method can significantly improve the accuracy of the skeleton obtained using the existing ITK three-dimensional thinning filter. The produced skeleton has laid the groundwork for accurate quantification of the angiogenesis. This is critical for the early detection of tumors and assessing anti-angiogenesis treatments.

Phenotypic clustering: a novel method for microglial morphology analysis

Background

Microglial cells are tissue-resident macrophages of the central nervous system. They are extremely dynamic, sensitive to their microenvironment and present a characteristic complex and heterogeneous morphology and distribution within the brain tissue. Many experimental clues highlight a strong link between their morphology and their function in response to aggression. However, due to their complex “dendritic-like” aspect that constitutes the major pool of murine microglial cells and their dense network, precise and powerful morphological studies are not easy to realize and complicate correlation with molecular or clinical parameters.

Methods

Using the knock-in mouse model CX3CR1^GFP/+, we developed a 3D automated confocal tissue imaging system coupled with morphological modelling of many thousands of microglial cells revealing precise and quantitative assessment of major cell features: cell density, cell body area, cytoplasm area and number of primary, secondary and tertiary processes. We determined two morphological criteria that are the complexity index (CI) and the covered environment area (CEA) allowing an innovative approach lying in (i) an accurate and objective study of morphological changes in healthy or pathological condition, (ii) an in situ mapping of the microglial distribution in different neuroanatomical regions and (iii) a study of the clustering of numerous cells, allowing us to discriminate different sub-populations.

Results

Our results on more than 20,000 cells by condition confirm at baseline a regional heterogeneity of the microglial distribution and phenotype that persists after induction of neuroinflammation by systemic injection of lipopolysaccharide (LPS). Using clustering analysis, we highlight that, at resting state, microglial cells are distributed in four microglial sub-populations defined by their CI and CEA with a regional pattern and a specific behaviour after challenge.

Conclusions

Our results counteract the classical view of a homogenous regional resting state of the microglial cells within the brain. Microglial cells are distributed in different defined sub-populations that present specific behaviour after pathological challenge, allowing postulating for a cellular and functional specialization. Moreover, this new experimental approach will provide a support not only to neuropathological diagnosis but also to study microglial function in various disease models while reducing the number of animals needed to approach the international ethical statements.

Electronic supplementary material

The online version of this article (doi:10.1186/s12974-016-0614-7) contains supplementary material, which is available to authorized users.

A portable fluorescence spectroscopy imaging system for automated root phenotyping in soil cores in the field

Root architecture traits are a target for pre-breeders. Incorporation of root architecture traits into new cultivars requires phenotyping. It is attractive to rapidly and directly phenotype root architecture in the field, avoiding laboratory studies that may not translate to the field. A combination of soil coring with a hydraulic push press and manual core-break counting can directly phenotype root architecture traits of depth and distribution in the field through to grain development, but large teams of people are required and labour costs are high with this method. We developed a portable fluorescence imaging system (BlueBox) to automate root counting in soil cores with image analysis software directly in the field. The lighting system was optimized to produce high-contrast images of roots emerging from soil cores. The correlation of the measurements with the root length density of the soil cores exceeded the correlation achieved by human operator measurements (R (2)=0.68 versus 0.57, respectively). A BlueBox-equipped team processed 4.3 cores/hour/person, compared with 3.7 cores/hour/person for the manual method. The portable, automated in-field root architecture phenotyping system was 16% more labour efficient, 19% more accurate, and 12% cheaper than manual conventional coring, and presents an opportunity to directly phenotype root architecture in the field as part of pre-breeding programs. The platform has wide possibilities to capture more information about root health and other root traits in the field.

High-content analysis of α-synuclein aggregation and cell death in a cellular model of Parkinson's disease

BACKGROUND

Alpha-synuclein (α-SYN) aggregates represent a key feature of Parkinson's disease, but the exact relationship between α-SYN aggregation and neurodegeneration remains incompletely understood. Therefore, the availability of a cellular assay that allows medium-throughput analysis of α-SYN-linked pathology will be of great value for studying the aggregation process and for advancing α-SYN-based therapies.

NEW METHOD

Here we describe a high-content neuronal cell assay that simultaneously measures oxidative stress-induced α-SYN aggregation and apoptosis.

RESULTS

We optimized an automated and reproducible assay to quantify both α-SYN aggregation and cell death in human SH-SY5Y neuroblastoma cells.

COMPARISON WITH EXISTING METHODS

Quantification of α-SYN aggregates in cells has typically relied on manual imaging and counting or cell-free assays, which are time consuming and do not allow a concurrent analysis of cell viability. Our high-content analysis method for quantification of α-SYN aggregation allows simultaneous measurements of multiple cell parameters at a single-cell level in a fast, objective and automated manner.

CONCLUSIONS

The presented analysis approach offers a rapid, objective and multiparametric approach for the screening of compounds and genes that might alter α-SYN aggregation and/or toxicity.

Automatic Dendritic Length Quantification for High Throughput Screening of Mature Neurons

High-throughput automated fluorescent imaging and screening are important for studying neuronal development, functions, and pathogenesis. An automatic approach of analyzing images acquired in automated fashion, and quantifying dendritic characteristics is critical for making such screens high-throughput. However, automatic and effective algorithms and tools, especially for the images of mature mammalian neurons with complex arbors, have been lacking. Here, we present algorithms and a tool for quantifying dendritic length that is fundamental for analyzing growth of neuronal network. We employ a divide-and-conquer framework that tackles the challenges of high-throughput images of neurons and enables the integration of multiple automatic algorithms. Within this framework, we developed algorithms that adapt to local properties to detect faint branches. We also developed a path search that can preserve the curvature change to accurately measure dendritic length with arbor branches and turns. In addition, we proposed an ensemble strategy of three estimation algorithms to further improve the overall efficacy. We tested our tool on images for cultured mouse hippocampal neurons immunostained with a dendritic marker for high-throughput screen. Results demonstrate the effectiveness of our proposed method when comparing the accuracy with previous methods. The software has been implemented as an ImageJ plugin and available for use.

Automated detection of soma location and morphology in neuronal network cultures

Automated identification of the primary components of a neuron and extraction of its sub-cellular features are essential steps in many quantitative studies of neuronal networks. The focus of this paper is the development of an algorithm for the automated detection of the location and morphology of somas in confocal images of neuronal network cultures. This problem is motivated by applications in high-content screenings (HCS), where the extraction of multiple morphological features of neurons on large data sets is required. Existing algorithms are not very efficient when applied to the analysis of confocal image stacks of neuronal cultures. In addition to the usual difficulties associated with the processing of fluorescent images, these types of stacks contain a small number of images so that only a small number of pixels are available along the z-direction and it is challenging to apply conventional 3D filters. The algorithm we present in this paper applies a number of innovative ideas from the theory of directional multiscale representations and involves the following steps: (i) image segmentation based on support vector machines with specially designed multiscale filters; (ii) soma extraction and separation of contiguous somas, using a combination of level set method and directional multiscale filters. We also present an approach to extract the soma's surface morphology using the 3D shearlet transform. Extensive numerical experiments show that our algorithms are computationally efficient and highly accurate in segmenting the somas and separating contiguous ones. The algorithms presented in this paper will facilitate the development of a high-throughput quantitative platform for the study of neuronal networks for HCS applications.

Unique functional and structural properties of the LRRK2 protein ATP-binding pocket

Pathogenic mutations in the LRRK2 gene can cause late-onset Parkinson disease. The most common mutation, G2019S, resides in the kinase domain and enhances activity. LRRK2 possesses the unique property of cis-autophosphorylation of its own GTPase domain. Because high-resolution structures of the human LRRK2 kinase domain are not available, we used novel high-throughput assays that measured both cis-autophosphorylation and trans-peptide phosphorylation to probe the ATP-binding pocket. We disclose hundreds of commercially available activity-selective LRRK2 kinase inhibitors. Some compounds inhibit cis-autophosphorylation more strongly than trans-peptide phosphorylation, and other compounds inhibit G2019S-LRRK2 more strongly than WT-LRRK2. Through exploitation of structure-activity relationships revealed through high-throughput analyses, we identified a useful probe inhibitor, SRI-29132 (11). SRI-29132 is exquisitely selective for LRRK2 kinase activity and is effective in attenuating proinflammatory responses in macrophages and rescuing neurite retraction phenotypes in neurons. Furthermore, the compound demonstrates excellent potency, is highly blood-brain barrier-permeant, but suffers from rapid first-pass metabolism. Despite the observed selectivity of SRI-29132, docking models highlighted critical interactions with residues conserved in many protein kinases, implying a unique structural configuration for the LRRK2 ATP-binding pocket. Although the human LRRK2 kinase domain is unstable and insoluble, we demonstrate that the LRRK2 homolog from ameba can be mutated to approximate some aspects of the human LRRK2 ATP-binding pocket. Our results provide a rich resource for LRRK2 small molecule inhibitor development. More broadly, our results provide a precedent for the functional interrogation of ATP-binding pockets when traditional approaches to ascertain structure prove difficult.

Efficient processing of fluorescence images using directional multiscale representations

Recent advances in high-resolution fluorescence microscopy have enabled the systematic study of morphological changes in large populations of cells induced by chemical and genetic perturbations, facilitating the discovery of signaling pathways underlying diseases and the development of new pharmacological treatments. In these studies, though, due to the complexity of the data, quantification and analysis of morphological features are for the vast majority handled manually, slowing significantly data processing and limiting often the information gained to a descriptive level. Thus, there is an urgent need for developing highly efficient automated analysis and processing tools for fluorescent images. In this paper, we present the application of a method based on the shearlet representation for confocal image analysis of neurons. The shearlet representation is a newly emerged method designed to combine multiscale data analysis with superior directional sensitivity, making this approach particularly effective for the representation of objects defined over a wide range of scales and with highly anisotropic features. Here, we apply the shearlet representation to problems of soma detection of neurons in culture and extraction of geometrical features of neuronal processes in brain tissue, and propose it as a new framework for large-scale fluorescent image analysis of biomedical data.

Cytometry in the brain: studying differentiation to diagnostic applications in brain disease and regeneration therapy

During brain development, a population of uniform embryonic cells migrates and differentiates into a large number of neural phenotypes - origin of the enormous complexity of the adult nervous system. Processes of cell proliferation, differentiation and programmed death of no longer required cells, do not occur only during embryogenesis, but are also maintained during adulthood and are affected in neurodegenerative and neuropsychiatric disease states. As neurogenesis is an endogenous response to brain injury, visible as proliferation (of to this moment silent stem or progenitor cells), its further stimulation can present a treatment strategy in addition to stem cell transfer for cell regeneration therapy. Concise techniques for studying such events in vitro and in vivo permit understanding of underlying mechanisms. Detection of subtle physiological alterations in brain cell proliferation and neurogenesis can be explored, that occur during environmental stimulation, exercise and ageing. Here, we have collected achievements in the field of basic research on applications of cytometry, including automated imaging for quantification of morphological or fluorescence-based parameters in cell cultures, towards imaging of three-dimensional brain architecture together with DNA content and proliferation data. Multi-parameter and more recently in vivo flow cytometry procedures, have been developed for quantification of phenotypic diversity and cell processes that occur during brain development as well as in adulthood, with importance for therapeutic approaches.

Statistical analysis of dendritic spine distributions in rat hippocampal cultures

BACKGROUND

Dendritic spines serve as key computational structures in brain plasticity. Much remains to be learned about their spatial and temporal distribution among neurons. Our aim in this study was to perform exploratory analyses based on the population distributions of dendritic spines with regard to their morphological characteristics and period of growth in dissociated hippocampal neurons. We fit a log-linear model to the contingency table of spine features such as spine type and distance from the soma to first determine which features were important in modeling the spines, as well as the relationships between such features. A multinomial logistic regression was then used to predict the spine types using the features suggested by the log-linear model, along with neighboring spine information. Finally, an important variant of Ripley's K-function applicable to linear networks was used to study the spatial distribution of spines along dendrites.

RESULTS

Our study indicated that in the culture system, (i) dendritic spine densities were "completely spatially random", (ii) spine type and distance from the soma were independent quantities, and most importantly, (iii) spines had a tendency to cluster with other spines of the same type.

CONCLUSIONS

Although these results may vary with other systems, our primary contribution is the set of statistical tools for morphological modeling of spines which can be used to assess neuronal cultures following gene manipulation such as RNAi, and to study induced pluripotent stem cells differentiated to neurons.

Validation of an algorithm to quantify changes in actin cytoskeletal organization

The actin cytoskeleton plays an important role in most, if not all, processes necessary for cell survival. Given the fundamental role that the actin cytoskeleton plays in the progression of cancer, it is an ideal target for chemotherapy. Although it is possible to image the actin cytoskeleton in a high-throughput manner, there is currently no validated method to quantify changes in the cytoskeleton in the same capacity, which makes research into its organization and the development of anticytoskeletal drugs difficult. We have validated the use of a linear feature detection algorithm, allowing us to measure changes in actin filament organization. Its ability to quantify changes associated with cytoskeletal disruption will make it a valuable tool in the development of compounds that target the cytoskeleton in cancer. Our results show that this algorithm can quantify cytoskeletal changes in a cell-based system after addition of both well-established and novel anticytoskeletal agents using either fluorescence microscopy or a high-content imaging approach. This novel method gives us the potential to screen compounds in a high-throughput manner for cancer and other diseases in which the cytoskeleton plays a key role.

Automated and accurate detection of soma location and surface morphology in large-scale 3D neuron images

Automated and accurate localization and morphometry of somas in 3D neuron images is essential for quantitative studies of neural networks in the brain. However, previous methods are limited in obtaining the location and surface morphology of somas with variable size and uneven staining in large-scale 3D neuron images. In this work, we proposed a method for automated soma locating in large-scale 3D neuron images that contain relatively sparse soma distributions. This method involves three steps: (i) deblocking the image with overlap between adjacent sub-stacks; (ii) locating the somas in each small sub-stack using multi-scale morphological close and adaptive thresholds; and (iii) fusion of the repeatedly located somas in all sub-stacks. We also describe a new method for the accurate detection of the surface morphology of somas containing hollowness; this was achieved by improving the classical Rayburst Sampling with a new gradient-based criteria. Three 3D neuron image stacks of different sizes were used to quantitatively validate our methods. For the soma localization algorithm, the average recall and precision were greater than 93% and 96%, respectively. For the soma surface detection algorithm, the overlap of the volumes created by automatic detection of soma surfaces and manually segmenting soma volumes was more than 84% for 89% of all correctly detected somas. Our method for locating somas can reveal the soma distributions in large-scale neural networks more efficiently. The method for soma surface detection will serve as a valuable tool for systematic studies of neuron types based on neuron structure.

APP2: automatic tracing of 3D neuron morphology based on hierarchical pruning of a gray-weighted image distance-tree

MOTIVATION

Tracing of neuron morphology is an essential technique in computational neuroscience. However, despite a number of existing methods, few open-source techniques are completely or sufficiently automated and at the same time are able to generate robust results for real 3D microscopy images.

RESULTS

We developed all-path-pruning 2.0 (APP2) for 3D neuron tracing. The most important idea is to prune an initial reconstruction tree of a neuron's morphology using a long-segment-first hierarchical procedure instead of the original termini-first-search process in APP. To further enhance the robustness of APP2, we compute the distance transform of all image voxels directly for a gray-scale image, without the need to binarize the image before invoking the conventional distance transform. We also design a fast-marching algorithm-based method to compute the initial reconstruction trees without pre-computing a large graph. This method allows us to trace large images. We bench-tested APP2 on ~700 3D microscopic images and found that APP2 can generate more satisfactory results in most cases than several previous methods.

AVAILABILITY

The software has been implemented as an open-source Vaa3D plugin. The source code is available in the Vaa3D code repository http://vaa3d.org.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Automated condition-invariable neurite segmentation and synapse classification using textural analysis-based machine-learning algorithms

High-resolution live-cell imaging studies of neuronal structure and function are characterized by large variability in image acquisition conditions due to background and sample variations as well as low signal-to-noise ratio. The lack of automated image analysis tools that can be generalized for varying image acquisition conditions represents one of the main challenges in the field of biomedical image analysis. Specifically, segmentation of the axonal/dendritic arborizations in brightfield or fluorescence imaging studies is extremely labor-intensive and still performed mostly manually. Here we describe a fully automated machine-learning approach based on textural analysis algorithms for segmenting neuronal arborizations in high-resolution brightfield images of live cultured neurons. We compare performance of our algorithm to manual segmentation and show that it combines 90% accuracy, with similarly high levels of specificity and sensitivity. Moreover, the algorithm maintains high performance levels under a wide range of image acquisition conditions indicating that it is largely condition-invariable. We further describe an application of this algorithm to fully automated synapse localization and classification in fluorescence imaging studies based on synaptic activity. Textural analysis-based machine-learning approach thus offers a high performance condition-invariable tool for automated neurite segmentation.

High-content neurite development study using optically patterned substrates

The study of neurite guidance in vitro relies on the ability to reproduce the distribution of attractive and repulsive guidance molecules normally expressed in vivo. The identification of subtle variations in the neurite response to changes in the spatial distribution of extracellular molecules can be achieved by monitoring the behavior of cells on protein gradients. To do this, automated high-content screening assays are needed to quantify the morphological changes resulting from growth on gradients of guidance molecules. Here, we present the use of laser-assisted protein adsorption by photobleaching (LAPAP) to allow the fabrication of large-scale substrate-bound laminin-1 gradients to study neurite extension. We produced thousands of gradients of different slopes and analyzed the variations in neurite attraction of neuron-like cells (RGC-5). An image analysis algorithm processed bright field microscopy images, detecting each cell and quantifying the soma centroid and the initiation, terminal and turning angles of the longest neurite.

MicroRNA-143 expression in dorsal root ganglion neurons

The unpleasant sensory and emotional experience of pain is initiated by excitation of primary afferent nociceptive neurons. Nerve damage or inflammation induces changes in nociceptive DRG neurons which contribute to both peripheral and central sensitization of pain-sensitive pathways. Recently, blockade of microRNA synthesis has been found to modulate the response of nociceptive neurons to inflammatory stimuli. However, little is known about the contributions of individual miRNAs to painful conditions. We compared miRNA expression in mouse sensory neurons and focussed on the localisation and control of miR-143. Using miRNA-arrays we compared the microRNA expression profile of intact lumbar DRG with one-day-old DRG cultures and found that nine miRNAs including miR-143 showed lower expression levels in cultures. Subsequent RT-qPCR confirmed array data and in-situ hybridisation localised miR-143 in the cytosol of sensory DRG neurons in situ and in vitro. Analysis of microbead-enriched neuron cultures showed significantly higher expression levels of miR-143 in isolectin B4 (I-B4) binding sensory neurons compared with neurons in the I-B4 negative flow-through fraction. In animal models of peripheral inflammation (injection of Complete Freund's Adjuvant, CFA) and nerve damage (transection of the sciatic nerve), we found that expression levels of miR-143 were significantly lower in DRGs ipsilateral to CFA injection or after nerve damage. Taken together, our data demonstrate for the first time miR-143 expression in nociceptive neurons. Since expression levels of miR-143 were higher in I-B4 positive neurons and declined in response to inflammation but not axotomy, miR-143 could selectively contribute to mRNA regulation in specific populations of nociceptors.

The Rho kinase inhibitor Fasudil up-regulates astrocytic glutamate transport subsequent to actin remodelling in murine cultured astrocytes

BACKGROUND AND PURPOSE

Glutamate transporters play a major role in maintaining brain homeostasis and the astrocytic transporters, EAAT1 and EAAT2, are functionally dominant. Astrocytic excitatory amino acid transporters (EAATs) play important roles in various neuropathologies wherein astrocytes undergo cytoskeletal changes. Astrocytic plasticity is well documented, but the interface between EAAT function, actin and the astrocytic cytoskeleton is poorly understood. Because Rho kinase (ROCK) is a key determinant of actin polymerization, we investigated the effects of ROCK inhibitors on EAAT activity and astrocytic morphology.

EXPERIMENTAL APPROACH

The functional activity of glutamate transport was determined in murine cultured astrocytes after exposure to the ROCK inhibitors Fasudil (HA-1077) and Y27632 using biochemical, molecular and morphological approaches. Cytochemical analyses assessed changes in astrocytic morphology, F-/G-actin, and localizations of EAAT1/2.

RESULTS

Fasudil and Y27632 increased [(3)H]-D-aspartate (D-Asp) uptake into astrocytes, and the action of Fasudil was time-dependent and concentration-related. The rapid stellation of astrocytes (glial fibrillary acidic protein immunocytochemistry) induced by Fasudil was accompanied by reduced phalloidin staining of F-actin and increased V(max) for [(3)H]-D-Asp uptake. Immunoblotting after biotinylation demonstrated that Fasudil increased the expression of EAAT1 and EAAT2 on the cell surface. Immunocytochemistry indicated that Fasudil induced prominent labelling of astrocytic processes by EAAT1/2.

CONCLUSION AND IMPLICATIONS

These data show for the first time that ROCK plays a major role in determining the cell surface expression of EAAT1/2, providing new evidence for an association between transporter function and astrocytic phenotype. ROCK inhibitors, via the actin cytoskeleton, effect a consequent elevation of glutamate transporter function - this activity profile may contribute to their beneficial actions in neuropathologies.

Stages of neuronal morphological development in vitro--an automated assay

Following plating in vitro, neurons pass through a series of morphological stages as they adhere and mature. These morphological stage transitions can be monitored as a function of time to evaluate the relative health and development of neuronal cultures under different conditions. While morphological development is usually quite obvious to the experienced eye, it can often be difficult to quantify in a meaningful way. Morphology quantification typically relies on manual image measurement and can therefore be tedious, time consuming and prone to human error. Here we report the successful development of an automated process using the commercially available image analysis program MetaMorph(®) to analyze the morphology and quantify the growth of embryonic spinal motor neurons in vitro. Our process relied on the Neurite Outgrowth and Cell Scoring modules included in MetaMorph(®) and on analyzing the exported data with an algorithm written in MATLAB(®). We first adopted a series of stages of motor neuron development in vitro. Neurons were classified into these stages directly from the available output of MetaMorph(®) using the algorithm written in MATLAB(®). We validated the results of the automated analysis against a manual analysis of the same images and found no statistically significant difference between the two methods. When properly configured, automated image analysis with MetaMorph(®) is a rapid and reliable alternative to manual measurement and has the potential to accelerate the research process.

Automated Sholl analysis of digitized neuronal morphology at multiple scales: Whole cell Sholl analysis versus Sholl analysis of arbor subregions

The morphology of dendrites and the axon determines how a neuron processes and transmits information. Neurite morphology is frequently analyzed by Sholl analysis or by counting the total number of neurites and branch tips. However, the time and resources required to perform such analysis by hand is prohibitive for the processing of large data sets and introduces problems with data auditing and reproducibility. Furthermore, analyses performed by hand or using course-grained morphometric data extraction tools can obscure subtle differences in data sets because they do not store the data in a form that facilitates the application of multiple analytical tools. To address these shortcomings, we have developed a program (titled "Bonfire") to facilitate digitization of neurite morphology and subsequent Sholl analysis. Our program builds upon other available open-source morphological analysis tools by performing Sholl analysis on subregions of the neuritic arbor, enabling the detection of local level changes in dendrite and axon branching behavior. To validate this new tool, we applied Bonfire analysis to images of hippocampal neurons treated with 25 ng/ml brain-derived neurotrophic factor (BDNF) and untreated control neurons. Consistent with prior findings, conventional Sholl analysis revealed that global exposure to BDNF increases the number of neuritic intersections proximal to the soma. Bonfire analysis additionally uncovers that BDNF treatment affects both root processes and terminal processes with no effect on intermediate neurites. Taken together, our data suggest that global exposure of hippocampal neurons to BDNF results in a reorganization of neuritic segments within their arbors, but not necessarily a change in their number or length. These findings were only made possible by the neurite-specific Sholl data returned by Bonfire analysis.

Gab2 regulates cytoskeletal organization and migration of mammary epithelial cells by modulating RhoA activation

The oncogenic signal transducer Gab2 mediates altered cytoskeletal organization and enhanced cell migration of mammary epithelial cells via down-regulation of RhoA activity. This sheds new light on the role of Gab2 in cancer cell metastasis.

The docking protein Gab2 is overexpressed in several human malignancies, including breast cancer, and is associated with increased metastatic potential. Here we report that Gab2 overexpression in MCF-10A mammary epithelial cells led to delayed cell spreading, a decrease in stress fibers and mature focal adhesions, and enhanced cell migration. Expression of a Gab2 mutant uncoupled from 14-3-3-mediated negative feedback (Gab2^2×A) led to a more mesenchymal morphology and acquisition of invasive potential. Expression of either Gab2 or Gab2^2×A led to decreased activation of RhoA, but only the latter increased levels of Rac-GTP. Expression of constitutively active RhoA in MCF-10A/Gab2 cells restored stress fibers and focal adhesions, indicating that Gab2 signals upstream of RhoA to suppress these structures. Mutation of the two Shp2-binding sites to phenylalanine (Gab2^ΔShp2) markedly reduced the effects of Gab2 on cellular phenotype and RhoA activation. Expression of Gab2 or Gab2^2×A, but not Gab2^ΔShp2, promoted Vav2 phosphorylation and plasma membrane recruitment of p190A RhoGAP. Knockdown of p190A RhoGAP reversed Gab2-mediated effects on stress fibers and focal adhesions. The identification of a novel pathway downstream of Gab2 involving negative regulation of RhoA by p190A RhoGAP sheds new light on the role of Gab2 in cancer progression.

Automated reconstruction of neuronal morphology: an overview

Digital reconstruction of neuronal morphology is a powerful technique for investigating the nervous system. This process consists of tracing the axonal and dendritic arbors of neurons imaged by optical microscopy into a geometrical format suitable for quantitative analysis and computational modeling. Algorithmic automation of neuronal tracing promises to increase the speed, accuracy, and reproducibility of morphological reconstructions. Together with recent breakthroughs in cellular imaging and accelerating progress in optical microscopy, automated reconstruction of neuronal morphology will play a central role in the development of high throughput screening and the acquisition of connectomic data. Yet, despite continuous advances in image processing algorithms, to date manual tracing remains the overwhelming choice for digitizing neuronal morphology. We summarize the issues involved in automated reconstruction, overview the available techniques, and provide a realistic assessment of future perspectives.

Neuron tracing in perspective

The study of the structure and function of neuronal cells and networks is of crucial importance in the endeavor to understand how the brain works. A key component in this process is the extraction of neuronal morphology from microscopic imaging data. In the past four decades, many computational methods and tools have been developed for digital reconstruction of neurons from images, with limited success. As witnessed by the growing body of literature on the subject, as well as the organization of challenging competitions in the field, the quest for a robust and fully automated system of more general applicability still continues. The aim of this work, is to contribute by surveying recent developments in the field for anyone interested in taking up the challenge. Relevant aspects discussed in the article include proposed image segmentation methods, quantitative measures of neuronal morphology, currently available software tools for various related purposes, and morphology databases. (c) 2010 International Society for Advancement of Cytometry.

HCA-vision: Automated neurite outgrowth analysis

Automating the analysis of neurons in culture represents a key aspect of the search for neuroactive compounds. A number of commercial neurite analysis software packages tend to measure some basic features such as total neurite length and number of branching points. However, with only these measurements, some differences between neurite morphologies that are clear to a human observer cannot be identified. The authors have developed a suite of image analysis tools that will allow researchers to produce quality analyses at primary screening rates. The suite provides sensitive and information-rich measurements of neurons and neurites. It can discriminate subtle changes in complex neurite arborization even when neurons and neurites are dense. This allows users to selectively screen for compounds triggering different types of neurite outgrowth behavior. In mixed cell populations, neurons can be filtered and separated from other brain cell types so that neurite analysis can be performed only on neurons. It supports batch processing with a built-in database to store the batch-processing results, a batch result viewer, and an ad hoc query builder for users to retrieve features of interest. The suite of tools has been deployed into a software package called HCA-Vision. The free version of the software package is available at http://www.hca-vision.com.

Automatic robust neurite detection and morphological analysis of neuronal cell cultures in high-content screening

Cell-based high content screening (HCS) is becoming an important and increasingly favored approach in therapeutic drug discovery and functional genomics. In HCS, changes in cellular morphology and biomarker distributions provide an information-rich profile of cellular responses to experimental treatments such as small molecules or gene knockdown probes. One obstacle that currently exists with such cell-based assays is the availability of image processing algorithms that are capable of reliably and automatically analyzing large HCS image sets. HCS images of primary neuronal cell cultures are particularly challenging to analyze due to complex cellular morphology. Here we present a robust method for quantifying and statistically analyzing the morphology of neuronal cells in HCS images. The major advantages of our method over existing software lie in its capability to correct non-uniform illumination using the contrast-limited adaptive histogram equalization method; segment neuromeres using Gabor-wavelet texture analysis; and detect faint neurites by a novel phase-based neurite extraction algorithm that is invariant to changes in illumination and contrast and can accurately localize neurites. Our method was successfully applied to analyze a large HCS image set generated in a morphology screen for polyglutamine-mediated neuronal toxicity using primary neuronal cell cultures derived from embryos of a Drosophila Huntington's Disease (HD) model.

DeadEasy neurons: automatic counting of HB9 neuronal nuclei in Drosophila

Research into the genetic basis of nervous system development and neurodegenerative diseases requires counting neurons to find out the extent of neurogenesis or neuronal loss. Drosophila is a widely used model organism for in vivo studies. However, counting neurons throughout the nervous system of the intact animal is humanly unfeasible. Automatic methods for cell counting in intact Drosophila are desirable. Here, we show a method called DeadEasy Neurons to count the number of neurons stained with anti-HB9 antibodies in Drosophila embryos. DeadEasy Neurons employs image filtering and mathematical morphology techniques in 2D and 3D, followed by identification of nuclei in 3D based on minimum volume, to count automatically the number of HB9 neurons in vivo. The resultant method has been validated for Drosophila embryos and we show here how it can be used to address biological questions. Counting neurons with DeadEasy is very fast, extremely accurate, and objective, and it enables analyses otherwise humanly unmanageable. DeadEasy Neurons can be modified by the user for other applications, and it will be freely available as an ImageJ plug-in. DeadEasy Neurons will be of interest to the microscopy, image processing, Drosophila, neurobiology, and biomedical communities.

Effects of substrate stiffness and cell density on primary hippocampal cultures

Previous studies have shown that dendrites are influenced by substrate stiffness when neurons are plated in either pure or mixed cultures. However, because substrate rigidity can also affect other aspects of culture development known to impact dendrite branching, such as overall cell number, it is unclear whether substrate stiffness exerts a direct or indirect effect on dendrite morphology. In this study, we determine whether substrate stiffness plays a critical role in regulating dendrite branching independent of cell number. We plated primary mixed hippocampal cultures on soft and stiff gels, with Young's moduli of 1 kPa and 7 kPa, respectively. We found that neurons plated on stiffer substrates showed increased branching relative to neurons grown on softer substrates at the same cell number. On the stiff gels, we also observed a cell number-dependent effect, in which increasing initial plating density decreased dendrite branching. This change correlates with an increase in extracellular glutamate. We concluded that both cell number and substrate stiffness play roles in determining dendrite branching, and that the two effects are independent of one another.

Precise fluorophore lifetime mapping in live-cell, multi-photon excitation microscopy

Fluorophore excited state lifetime is a useful indicator of micro-environment in cellular optical molecular imaging. For quantitative sensing, precise lifetime determination is important, yet is often difficult to accomplish when using the experimental conditions favored by live cells. Here we report the first application of temporal optimization and spatial denoising methods to two-photon time-correlated single photon counting (TCSPC) fluorescence lifetime imaging microscopy (FLIM) to improve lifetime precision in live-cell images. The results demonstrated a greater than five-fold improvement in lifetime precision. This approach minimizes the adverse effects of excitation light on live cells and should benefit FLIM applications to high content analysis and bioimage informatics.

Quantitative neurite outgrowth measurement based on image segmentation with topological dependence

The study of neuronal morphology and neurite outgrowth has been enhanced by the combination of imaging informatics and high content screening, in which thousands of images are acquired using robotic fluorescent microscopy. To understand the process of neurite outgrowth in the context of neuroregeneration, we used mouse neuroblastoma N1E115 as our model neuronal cell. Six-thousand cellular images of four different culture conditions were acquired with two-channel widefield fluorescent microscopy. We developed a software package called NeuronCyto. It is a fully automatic solution for neurite length measurement and complexity analysis. A novel approach based on topological analysis is presented to segment cells. The detected nuclei were used as references to initialize the level set function. Merging and splitting of cells segments were prevented using dynamic watershed lines based on the constraint of topological dependence. A tracing algorithm was developed to automatically trace neurites and measure their lengths quantitatively on a cell-by-cell basis. NeuronCyto analyzes three important biologically relevant features, which are the length, branching complexity, and number of neurites. The application of NeuronCyto on the experiments of Toca-1 and serum starvation show that the transfection of Toca-1 cDNA induces longer neurites with more complexities than serum starvation.

Automated analysis of NeuronJ tracing data

Studies of neuronal differentiation in vitro often involve tracing and analysis of neurites. NeuronJ (Meijering et al., Cytometry Part A 2004;58A:167-176; http://www.imagescience.org/meijering/software/neuronj/) is a program that can be used for semiautomated tracing of individual neurons; when tracing is completed, a text file containing neurite length measurements is generated. Using cultured hippocampal neurons, we have found that to reach statistical significance it is generally necessary to trace about 100 neurons in each treatment group. Posttracing data analysis requires importing each text file into a statistics program. Analysis of distinct parameters, such as effects of a treatment on axonal versus dendritic branching, requires a great deal of time consuming posttracing data manipulation. We have developed XL_Calculations, a Java-based program that performs batch analysis on NeuronJ measurement files and automatically makes multiple calculations, including the number, length, and total output (sum length) of primary, secondary, and tertiary neurites on axons and dendrites, and writes the calculations into an Excel worksheet. Batch processing of NeuronJ measurement files dramatically reduces the time required to analyze neuronal morphology. In addition, our program performs more than 45 distinct calculations, enabling detailed determination of treatment effects on neuronal differentiation. Using this program to analyze NeuronJ tracing data, we demonstrate that continuous exposure of differentiating hippocampal neurons to Netrin 1 increases the number of secondary branches on both axons and dendrites, without significantly altering the length of the axon, dendrites, or branches. Similar results were obtained when neurons were grown on poly-D-lysine or laminin.

High-content analysis in neuroscience

High-content analysis (HCA) combines automated microscopy and automated image analysis to quantify complex cellular anatomy and biochemistry objectively, accurately and quickly. High-content assays that are applicable to neuroscience include those that can quantify various aspects of dendritic trees, protein aggregation, transcription factor translocation, neurotransmitter receptor internalization, neuron and synapse number, cell migration, proliferation and apoptosis. The data that are generated by HCA are rich and multiplexed. HCA thus provides a powerful high-throughput tool for neuroscientists.